PAPM: A Physics-aware Proxy Model for Process Systems

In the context of proxy modeling for process systems, traditional data-driven deep learning approaches frequently encounter significant challenges, such as substantial training costs induced by large amounts of data, and limited generalization capabilities. As a promising alternative, physics-aware models incorporate partial physics knowledge to ameliorate these challenges. Although demonstrating efficacy, they fall short in terms of exploration depth and universality. To address these shortcomings, we introduce a p hysics- a ware p roxy m odel ( PAPM ) that fully incorporates partial prior physics of process systems, which includes multiple input conditions and the general form of conservation relations, resulting in better out-of-sample generalization. Additionally, PAPM contains a holistic temporal-spatial stepping module for flexible adaptation across various process systems. Through systematic comparisons with state-of-the-art pure data-driven and physics-aware models across five two-dimensional benchmarks in nine generalization tasks, PAPM notably achieves an average performance improvement of 6. 7%, while requiring fewer FLOPs, and just 1% of the parameters compared to the prior leading method.

Authors

• Pengwei Liu
• Zhongkai Hao
• Xingyu Ren
• Hangjie Yuan
• Jiayang Ren
• Dong Ni 0002

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